Systematic methods for the computation of the directional fields and singular points of fingerprints

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint databas

A Multi-Scale Approach to Directional Field Estimation

This paper proposes a robust method for directional field estimation from fingerprint images that combines estimates at multiple scales. The method is able to provide accurate estimates in scratchy regions, while at the same time maintaining correct estimates around singular points. Compared to other methods, the penalty for detecting false singular points is much smaller, because this does not deteriorate the directional field estimate

Impact of Noisy Singular Point Detection on Performance of Fingerprint Matching

The performance of fingerprint matching has significantly improved in the recent times. However, this performance is still affected by many factors such as inadequate detection of singular points, poor-quality and noisy fingerprint images mostly result in spurious or missing singular points, which generally results in degradation of the overall performance of the fingerprint matching.   This paper presents the impact of noisy or spurious singular (core/delta) points on the performance of fingerprint matching. The algorithm comprises of image enhancement stage, the singular points extraction stage and post-processing stage. The image enhancement stage preprocessed the fingerprint images, the singular point extraction stage extracts the true and the noisy or false singular points, while the post processing stage eliminate the spurious singular point.  Benchmarked FVC2000, FVC2002, FVC2004 and FVC2006 fingerprint databases which comprise four datasets each were used for the experimental study. The completion time for the singular point extraction on each dataset were computed. The matching algorithm was also implemented to verify the impact of noisy singular points on false non match rate (FNMR), false match rate (FMR) and matching speed. The completion time extraction of singular points from the noisy fingerprint images is 263seconds whereas the completion time for extraction of true singular points is 82seconds. The increase in completion time is due to the inclusion of spurious features (noise/contaminants), whereas there is time decreases after the spurious features had been eliminated.  The obtained values and analysis revealed that poor and noisy quality fingerprint images have adverse effect on the performance of fingerprint matching. &nbsp

Combining and Steganography of 3D Face Textures

One of the serious issues in communication between people is hiding information from others, and the best way for this, is deceiving them. Since nowadays face images are mostly used in three dimensional format, in this paper we are going to steganography 3D face images, detecting which by curious people will be impossible. As in detecting face only its texture is important, we separate texture from shape matrices, for eliminating half of the extra information, steganography is done only for face texture, and for reconstructing 3D face, we can use any other shape. Moreover, we will indicate that, by using two textures, how two 3D faces can be combined. For a complete description of the process, first, 2D faces are used as an input for building 3D faces, and then 3D textures are hidden within other images.Comment: 6 pages, 10 figures, 16 equations, 5 section